Numerous procedures have been suggested for determining the number of factors to retain in factor analysis. However, previous studies have focused on comparing methods using normal data sets. This study had two phases. The first phase explored the Kaiser method, Scree test, Bartletts chi-square test, Minimum Average Partial (1976 & 2000), Horns parallel analysis, and Longmans Parallel Analysis on normal data using the estimation methods of Maximum Likelihood (ML), Principal Component Analysis (PCA), and Principal Factor Analysis (PFA). The second phase explored the Kaiser method, Scree test, Minimum Average Partial (1976 & 2000), and Horns parallel analysis, and Longmans Parallel Analysis on data that contained outliers using the estimation methods of PCA and PFA. In the first phase, sample correlation matrices were generated with varied conditions (sample size, number of variables, estimation methods). Three hundred sample correlation matrices were generated for each condition for a grand total of eighteen hundred. The performance of parallel analysis and the Kaiser method were generally the best across all situations. However, the increase in variables and sample size under each condition showed a difference in accuracy among the methods. The increase in sample size resulted in little difference between estimation methods of PCA and PFA. Recommendations concerning the accuracy of the methods under each condition are discussed. In the second phase, fifty sample correlation matrices were randomly selected from each of the three hundred sample correlations matrices under each condition. An outlier was randomly incorporated in each of the fifty sample correlation matrices. The squared Mahalanobis distance was recorded for each to determine the distance at which the methods start to fail. The research conducted here indicates that Parallel Analysis and Longmans Parallel Analysis was very resistant to outliers in some specific cases. However, it was evident from the data that each method tended to make the incorrect decision on retaining the correct number of factors when the squared Mahalanobis distance reached a certain amount. A discussion of method performance is given on each of the conditions to help determine the most effective and useful combinations on dealing with the outliers.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-10282009-144508 |
| Date | 29 October 2009 |
| Creators | Swaim, Victor Snipes |
| Contributors | Kennedy, Eugene, Hinson, Janice M., Wandersee, James H., Monlezun, Charles J., Hirschheim, Rudolf a |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-10282009-144508/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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